Share This Article:

Identification of essential language areas by combination of fMRI from different tasks using probabilistic independent component analysis

Abstract Full-Text HTML Download Download as PDF (Size:5976KB) PP. 157-162
DOI: 10.4236/jbise.2008.13026    5,138 Downloads   9,011 Views   Citations

ABSTRACT

Functional magnetic resonance imaging (fMRI) has been used to lateralize and localize lan-guage areas for pre-operative planning pur-poses. To identify the essential language areas from this kind of observation method, we pro-pose an analysis strategy to combine fMRI data from two different tasks using probabilistic in-dependent component analysis (PICA). The assumption is that the independent compo-nents separated by PICA identify the networks activated by both tasks. The results from a study of twelve normal subjects showed that a language-specific component was consistently identified, with the participating networks sepa-rated into different components. Compared with a model-based method, PICA’s ability to capture the neural networks whose temporal activity may deviate from the task timing suggests that PICA may be more appropriate for analyzing language fMRI data with complex event-related paradigms, and may be particularly helpful for patient studies. This proposed strategy has the potential to improve the correlation between fMRI and invasive techniques which can dem-onstrate essential areas and which remain the clinical gold standard.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Tie, Y. , O. Suarez, R. , Whalen, S. , H. Norton, I. and J. Golby, A. (2008) Identification of essential language areas by combination of fMRI from different tasks using probabilistic independent component analysis. Journal of Biomedical Science and Engineering, 1, 157-162. doi: 10.4236/jbise.2008.13026.

References

[1] J.R. Binder, S.J. Swanson, T.A. Hammeke, G.L. Morris, W.M. Mueller, M. Fischer, S. Benbadis, J.A. Frost, S.M. Rao, and V.M. Haughton, (1996) “Determination of language dominance using functional MRI: a comparison with the Wada test,” Neurology, vol. 46, pp. 978-984.
[2] C. Stippich, N. Rapps, J. Dreyhaupt, A. Durst, B. Kress, E. Nen-ning, V.M. Tronnier, and K. Sartor, (2007) “Localizing and later-alizing language in partients with brain tumors: feasibilty of rou-tine preoperative functional MR imaging in 81 consecutive pa-tients,” Radiology, vol. 243, pp. 828-836.
[3] S. Tharin and A. Golby, (2007) “Functional brain mapping and its applications to neurosurgery,” Neurosurgery, vol. 60, pp. 185-201.
[4] C.F. Beckmann and S.M. Smith, (2004) “Probabilistic independ-ent component analysis for functional magnetic resonance imag-ing,” IEEE Trans. Med. Imaging, vol. 23, pp. 137-152.
[5] M.J. McKeown, S. Makeig, G.G. Brown, T.P. Jung, S.S. Kinder-mann, A.J. Bell, and T.J. Sejnowski, (1998) “Analysis of fMRI data by blind separation into independent spatial components,” Hum. Brain Mapp., vol. 6, pp. 160-188.
[6] V.D. Calhoun, T. Adali, G.D. Pearlson, and J.J. Pekar, (2001) “A method for making group inferences from functional MRI data using independent component analysis,” Hum. Brain Mapp., vol. 14, pp. 140-151.
[7] Y. Tie, S. Whalen, R.O. Suarez, and A.J. Golby, (2008) “Group independent component analysis of language fMRI from word generation tasks,” Neuroimage, vol. 42, pp. 1214-1225.
[8] M.D. Greicius, G. Srivastava, A.L., Reiss, and V. Menon, (2004) “Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: Evidence form functional MRI,” Proc. Natl. Acad. Sci. U.S.A., vol. 101, pp. 4637-4642.
[9] C.F. Beckmann, M. DeLuca, J.T. Devlin, and S.M. Smith, (2005) “Investigations into resting-state connectivity using independent component analysis,” Phil. Trans. R. Soc. B, vol. 360, pp. 1001-1013.
[10] M. Fukunaga, S.G. Horovitz, P. Van Gelderen, J.A. de Zwart, J.M. Jansma, V.N. Ikonomidou, R. Chu, R.H.R. Deckers, D.A. Leo-pold, and J.H. Duyn, (2006) “Large-amplitude, spatially corre-lated fluctuations in BOLD fMRI signals during extended rest and early sleep stages,” Magn. Reson. Imaging, vol. 24, pp. 979-992.
[11] D. Sridharan, D. J. Levitin, C.H. Chafe, J. Berger, and V. Menon, (2007) “Neural dynamics of event segmentation in music: Con-verging evidence for dissociable ventral and dorsal networks,” Neuron, vol. 55, pp. 521-532.
[12] A. Hyvarinen, (1999) “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Trans. Neural. Netw., vol. 10, pp. 626-634.
[13] K.J. Friston, P. Fletcher, O. Josephs, A. Holmes, M.D. Rugg, and R. Turner, (1998) “Event-related fMRI: characterizing differential responses,” Neuroimage, vol. 7, pp. 30-40.
[14] A.I. Holodny, M. Schulder, W.C. Liu, J. Wolko, J.A. Maldjian, and A.J. Kalnin, (2000)“The effect of brain tumors on BOLD functional MR imaging activation in the adjacent motor cortex: implications for image-guided neurosurgery,” AJNR Am. J. Neu-roradiol., vol. 21, pp. 1415-1422.
[15] C.H. Moritz, B.P. Rogers, and M.E. Meyerand, (2003) “Power spectrum ranked independent component analysis of a periodic fMRI complex motor paradigm,” Hum. Brain Mapp, vol. 18, pp. 111-122.

  
comments powered by Disqus

Copyright © 2019 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.